OpenRaiser/Intern-Atlas huggingface dataset:
FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction
Venue: arXiv cs.RO 2025
Year: 2025

Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite category-level perceptual generalization, failing to predict the value of synergistic bimanual actions. We propose the Feature-Conditioned Bimanual Value Network (FCBV-Net), operating on 3D point clouds to specifically enhance category-level policy generalization for garment smoothing. FCBV-Net conditions bimanual action value prediction on pre-trained, frozen dense geometric features, ensuring robustness to intra-category garment variations. Trainable downstream components then learn a task-specific policy using these static features. In simulated GarmentLab experiments with the CLOTH3D dataset, FCBV-Net demonstrated superior category-level generalization. It exhibited only an 11.5% efficiency drop (Steps80) on unseen garments compared to 96.2% for a 2D image-based baseline, and achieved 89% final coverage, outperforming an 83% coverage from a 3D correspondence-based baseline that uses identical per-point geometric features but a fixed primitive. These results highlight that the decoupling of geometric understanding from bimanual action value learning enables better category-level generalization.
AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study
Venue: arXiv cs.HC 2025
Year: 2025

This paper focuses on AI tutors in foreign language learning, a field of application of AI tutors with great development, especially during the last years, when great advances in natural language understanding and processing in real time, have been achieved. These tutors attempt to address needs for improving language skills (speaking, or communicative competence, understanding). In this paper, a mixed-methos empirical study on the use of different kinds of state-of-the-art AI tutors for language learning is reported. This study involves a user experience evaluation of typical such tools, with special focus in their conversation functionality and an evaluation of their quality, based on chat transcripts. This study can help establish criteria for assessing the quality of such systems and inform the design of future tools, including concerns about data privacy and secure handling of learner information.
Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering
Venue: arXiv cs.CV 2025
Year: 2025

Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework.
Neural Estimation of Information Leakage for Secure Communication System Design
Venue: arXiv cs.IT 2025
Year: 2025

Underestimating the leakage can compromise secrecy, while overestimating it may lead to inefficient system design. Therefore, a reliable leakage estimator is essential. Neural network-based estimators provide a data-driven way to estimate mutual information without requiring full knowledge of the channel or source distributions. In this work, we aim to scale the blocklength of a wiretap code such that the estimator can still feasibly operate. We propose an improved mutual information estimator based on the variational contrastive log-ration upper bound framework, tailored for both discrete and continuous variables. By using a mixture of Bernoulli experts parameterized by neural networks, the estimator is able to quantify information leakage in communication systems, which employ complex data processing like universal hash family. We further propose a method to utilize the proposed estimator to design the universal hash family for a wiretap code or secret key generation design. Simulation results show thatprior methods significantly underestimate the mutual information, particularly when using universal hash family for higher blocklengths ($n\gg$16). The proposed method can scale the blocklength up to 255, and we conjecture that the design can scale well to even higher blocklengths given adequate training data and model size. Additionally, we contend that our proposed estimator and adaptive hash design framework offer a practical approach for extending physical layer security considerations for wiretap channels into the finite blocklength regime.
Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Venue: arXiv cs.CR 2025
Year: 2025

Timely and effective incident response is key to managing the growing frequency of cyberattacks. However, identifying the right response actions for complex systems is a major technical challenge. A promising approach to mitigate this challenge is to use the security knowledge embedded in large language models (LLMs) to assist security operators during incident handling. Recent research has demonstrated the potential of this approach, but current methods are mainly based on prompt engineering of frontier LLMs, which is costly and prone to hallucinations. We address these limitations by presenting a novel way to use an LLM for incident response planning with reduced hallucination. Our method includes three steps: fine-tuning, information retrieval, and lookahead planning. We prove that our method generates response plans with a bounded probability of hallucination and that this probability can be made arbitrarily small at the expense of increased planning time under certain assumptions. Moreover, we show that our method is lightweight and can run on commodity hardware. We evaluate our method on logs from incidents reported in the literature. The experimental results show that our method a) achieves up to 22% shorter recovery times than frontier LLMs and b) generalizes to a broad range of incident types and response actions.
EvoGraph: Hybrid Directed Graph Evolution toward Software 3.0
Venue: arXiv cs.SE 2025
Year: 2025

We introduce **EvoGraph**, a framework that enables software systems to evolve their own source code, build pipelines, documentation, and tickets. EvoGraph represents every artefact in a typed directed graph, applies learned mutation operators driven by specialized small language models (SLMs), and selects survivors with a multi-objective fitness. On three benchmarks, EvoGraph fixes 83% of known security vulnerabilities, translates COBOL to Java with 93% functional equivalence (test verified), and maintains documentation freshness within two minutes. Experiments show a 40% latency reduction and a sevenfold drop in feature lead time compared with strong baselines. We extend our approach to **evoGraph**, leveraging language-specific SLMs for modernizing .NET, Lisp, CGI, ColdFusion, legacy Python, and C codebases, achieving 82-96% semantic equivalence across languages while reducing computational costs by 90% compared to large language models. EvoGraph's design responds to empirical failure modes in legacy modernization, such as implicit contracts, performance preservation, and integration evolution. Our results suggest a practical path toward Software 3.0, where systems adapt continuously yet remain under measurable control.
FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance
Venue: arXiv cs.LG 2025
Year: 2025

Hallucination remains a critical challenge for deploying Large Language Models (LLMs) in finance. Accurate extraction and precise calculation from tabular data are essential for reliable financial analysis, since even minor numerical errors can undermine decision-making and regulatory compliance. Financial applications have unique requirements, often relying on context-dependent, numerical, and proprietary tabular data that existing hallucination benchmarks rarely capture. In this study, we develop a rigorous and scalable framework for evaluating intrinsic hallucinations in financial LLMs, conceptualized as a context-aware masked span prediction task over real-world financial documents. Our main contributions are: (1) a novel, automated dataset creation paradigm using a masking strategy; (2) a new hallucination evaluation dataset derived from S&P 500 annual reports; and (3) a comprehensive evaluation of intrinsic hallucination patterns in state-of-the-art LLMs on financial tabular data. Our work provides a robust methodology for in-house LLM evaluation and serves as a critical step toward building more trustworthy and reliable financial Generative AI systems.
Dancing with a Robot: An Experimental Study of Child-Robot Interaction in a Performative Art Setting
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents an evaluation of 18 children's in-the-wild experiences with the autonomous robot arm performer NED (Never-Ending Dancer) within the Thingamabobas installation, showcased across the UK. We detail NED's design, including costume, behaviour, and human interactions, all integral to the installation. Our observational analysis revealed three key challenges in child-robot interactions: 1) Initiating and maintaining engagement, 2) Lack of robot expressivity and reciprocity, and 3) Unmet expectations. Our findings show that children are naturally curious, and adept at interacting with a robotic art performer. However, our observations emphasise the critical need to optimise human-robot interaction (HRI) systems through careful consideration of audience's capabilities, perceptions, and expectations, within the performative arts context, to enable engaging and meaningful experiences, especially for young audiences.
ML-based Short Physical Performance Battery future score prediction based on questionnaire data
Venue: arXiv cs.LG 2025
Year: 2025

Effective slowing down of older adults\' physical capacity deterioration requires intervention as soon as the first symptoms surface. In this paper, we analyze the possibility of predicting the Short Physical Performance Battery (SPPB) score at a four-year horizon based on questionnaire data. The ML algorithms tested included Random Forest, XGBoost, Linear Regression, dense and TabNet neural networks. The best results were achieved for the XGBoost (mean absolute error of 0.79 points). Based on the Shapley values analysis, we selected smaller subsets of features (from 10 to 20) and retrained the XGBoost regressor, achieving a mean absolute error of 0.82.
Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2
Venue: arXiv cs.CV 2025
Year: 2025

Segmenting gas bubbles in multiphase flows is a critical yet unsolved challenge in numerous industrial settings, from metallurgical processing to maritime drag reduction. Traditional approaches-and most recent learning-based methods-assume near-spherical shapes, limiting their effectiveness in regimes where bubbles undergo deformation, coalescence, or breakup. This complexity is particularly evident in air lubrication systems, where coalesced bubbles form amorphous and topologically diverse patches. In this work, we revisit the problem through the lens of modern vision foundation models. We cast the task as a transfer learning problem and demonstrate, for the first time, that a fine-tuned Segment Anything Model SAM v2.1 can accurately segment highly non-convex, irregular bubble structures using as few as 100 annotated images.
Navigating the Trade-off: A Synthesis of Defensive Strategies for Zero-Shot Adversarial Robustness in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

This report synthesizes eight seminal papers on the zero-shot adversarial robustness of vision-language models (VLMs) like CLIP. A central challenge in this domain is the inherent trade-off between enhancing adversarial robustness and preserving the model's zero-shot generalization capabilities. We analyze two primary defense paradigms: Adversarial Fine-Tuning (AFT), which modifies model parameters, and Training-Free/Test-Time Defenses, which preserve them. We trace the evolution from alignment-preserving methods (TeCoA) to embedding space re-engineering (LAAT, TIMA), and from input heuristics (AOM, TTC) to latent-space purification (CLIPure). Finally, we identify key challenges and future directions including hybrid defense strategies and adversarial pre-training.
Pruning Large Language Models by Identifying and Preserving Functional Networks
Venue: arXiv cs.CL 2025
Year: 2025

Structured pruning is one of the representative techniques for compressing large language models (LLMs) to reduce GPU memory consumption and accelerate inference speed. It offers significant practical value in improving the efficiency of LLMs in real-world applications. Current structured pruning methods typically rely on assessment of the importance of the structure units and pruning the units with less importance. Most of them overlooks the interaction and collaboration among artificial neurons that are crucial for the functionalities of LLMs, leading to a disruption in the macro functional architecture of LLMs and consequently a pruning performance degradation. Inspired by the inherent similarities between artificial neural networks and functional neural networks in the human brain, we alleviate this challenge and propose to prune LLMs by identifying and preserving functional networks within LLMs in this study. To achieve this, we treat an LLM as a digital brain and decompose the LLM into functional networks, analogous to identifying functional brain networks in neuroimaging data. Afterwards, an LLM is pruned by preserving the key neurons within these functional networks. Experimental results demonstrate that the proposed method can successfully identify and locate functional networks and key neurons in LLMs, enabling efficient model pruning. Our code is available at https://github.com/WhatAboutMyStar/LLM_ACTIVATION.
A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.
Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models
Venue: arXiv cs.LG 2025
Year: 2025

Marine chlorophyll concentration is an important indicator of ecosystem health and carbon cycle strength, and its accurate prediction is crucial for red tide warning and ecological response. In this paper, we propose a LSTM-RF hybrid model that combines the advantages of LSTM and RF, which solves the deficiencies of a single model in time-series modelling and nonlinear feature portrayal. Trained with multi-source ocean data(temperature, salinity, dissolved oxygen, etc.), the experimental results show that the LSTM-RF model has an R^2 of 0.5386, an MSE of 0.005806, and an MAE of 0.057147 on the test set, which is significantly better than using LSTM (R^2 = 0.0208) and RF (R^2 =0.4934) alone , respectively. The standardised treatment and sliding window approach improved the prediction accuracy of the model and provided an innovative solution for high-frequency prediction of marine ecological variables.
Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection
Venue: arXiv cs.CV 2025
Year: 2025

Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management. Despite the remarkable progress of deep learning in recent years, most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions. We observe that frequency-domain feature modeling particularly in the wavelet domain an amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain. Thus, we propose a method called Wavelet-Guided Dual-Frequency Encoding (WGDF). Specifically, we first apply Discrete Wavelet Transform (DWT) to decompose the input images into high-frequency and low-frequency components, which are used to model local details and global structures, respectively. In the high-frequency branch, we design a Dual-Frequency Feature Enhancement (DFFE) module to strengthen edge detail representation and introduce a Frequency-Domain Interactive Difference (FDID) module to enhance the modeling of fine-grained changes. In the low-frequency branch, we exploit Transformers to capture global semantic relationships and employ a Progressive Contextual Difference Module (PCDM) to progressively refine change regions, enabling precise structural semantic characterization. Finally, the high- and low-frequency features are synergistically fused to unify local sensitivity with global discriminability. Extensive experiments on multiple remote sensing datasets demonstrate that WGDF significantly alleviates edge ambiguity and achieves superior detection accuracy and robustness compared to state-of-the-art methods. The code will be available at https://github.com/boshizhang123/WGDF.
A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
Venue: arXiv cs.HC 2025
Year: 2025

This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how cultural, social, and contextual factors influence users' understanding of fairness, transparency, and accountability in AI systems. Our findings reveal nuanced attitudes toward human oversight, explanation mechanisms, and contestability, highlighting the importance of culturally aware design principles for equitable and trustworthy algorithmic systems. These insights contribute to ongoing discussions on algorithmic fairness by foregrounding perspectives from a non-Western context, thus broadening the global dialogue on ethical AI deployment.
Difference Views for Visual Graph Query Building
Venue: arXiv cs.IR 2025
Year: 2025

Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing visual query builders enable non-expert users to construct SPARQL queries and utilize the knowledge contained in these graphs. Query building is, however, an iterative and, often, visual process where the question of the user can change and differ throughout the process, especially for explorative search. Our visual querying interface communicates these change between iterative steps in the query building process using graph differences to contrast the changes and the evolution in the graph query. We also enable users to formulate their evolving information needs using a natural language interface directly integrated into the difference query view. We, furthermore, communicate the change in results in the result view by contrasting the differences in both result distribution and individual instances of the prototype graph and demonstrate the system's applicability through case studies on different ontologies and usage scenarios, illustrating how our system fosters, both, data exploration and analysis of domain-specific graphs.
$\mathbb{F}_{2}\mathbb{F}_{4}$-Additive Complementary Dual Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we investigate the structure and properties of additive complementary dual (ACD) codes over the mixed alphabet $\mathbb{F}_2\mathbb{F}_4$ relative to a certain inner product defined over $\mathbb{F}_2\mathbb{F}_4$. We establish sufficient conditions under which such codes are additive complementary dual (ACD) codes. We also show that ACD codes over $\mathbb{F}_{2}\mathbb{F}_{4}$ can be applied to construct binary linear complementary dual codes as their images under the linear map $W$. Notably, we prove that if the binary image of a code is LCD, then the original code is necessarily ACD. An example is given where the image is a distance-optimal binary LCD code.
Implementation and Application of Multi-Format 3D Data Integration in a Cross-Device Commercial Metaverse Platform
Venue: arXiv cs.HC 2025
Year: 2025

Traditionally, specialized 3D design data, such as BIM and CAD, have been accessible only to a select group of experts, creating significant barriers that prevent general users from participating in decision-making processes. This paper provides a systematic overview of practical insights for utilizing 3D data in industrial and architectural domains by presenting implementation cases of the industrial metaverse on Cluster, a commercial cross-device metaverse platform. This paper analyzes the characteristics and constraints of major data formats in the industrial and architectural fields and organizes integration workflows for the metaverse. Through application cases utilizing 3D data across multiple domains, we present practical examples of collaborative decision-making support enabled by the fusion of metaverse and digital twin technologies. Specifically, we demonstrate that multi-device access and simultaneous multi-user participation capabilities foster democratic environments in the industrial metaverse, which are challenging to achieve with conventional, expert-dependent systems.